[1]} presented a visual navigation framework for row crop fields. The system only uses onboard cameras for navigation without performing explicit localization. The switching from one raw to another is also implemented in the same framework. Researchers have identified that the agricultural environments contain lack of reliable sensor measurements to detect distinguishable landmarks, which causes high visual aliasing. They also state that the constantly growing crops make it difficult to maintain a fixed map of the environment. Their robot could: autonomously navigate through row crop fields without maintaining any global reference maps, monitor crops in the fields with high coverage by accurately following the crop rows, and the system is robust to fields with various row structures and characteristics. Crop row detection was based on the Excess Green Index calculation. Their crop row detection scenario is based on a field setup which doesn't have any weeds. The robotic platform is equipped with two cameras (front and back) to perform the crop transition. Their work indicates that the vision-based navigation in crop rows could be performed without the need for a global map.
